Machine learning in predicting cauda equina imaging outcomes- a solution to the problem.

Purpose: Cauda Equina Syndrome (CES) is a rare surgical emergency. The implications for loss of quality of life through delayed management are high, though no clinical symptom is pathognomonic in its diagnosis. We describe how machine learning based algorithms can be used in triaging patients with suspected CES (CES-S).

Ambulatory bariatric surgery: a prospective single-center experience.

Background: Ambulatory bariatric surgery has recently gained interest especially as a potential way to improve access for eligible patients with severe obesity. Building on our previously published research, this follow-up study delves deeper in the evolving landscape of ambulatory bariatric surgery over a 3-year period, focusing on predictors of success/failure.

Methods: In a prospective single-center follow-up study, we conducted a descriptive assessment of all eligible patients as per our established protocol, who underwent a planned same-day discharge (SDD) primary sleeve gastrectomy (SG) or Roux-en-Y gastric bypass (RYGB) between 03/01/2021 and 02/29/2024.

Validation of the Individualized Metabolic Surgery score in predicting long-term remission of diabetes after duodenal switch-type procedures.

Aim: To validate the Individualized Metabolic Surgery (IMS) score and assess long-term remission of type 2 diabetes (T2D) after duodenal switch (DS)-type procedures in patients with obesity. In addition, to help guide metabolic procedure selection for those patients categorized as having severe T2D.

Materials And Methods: This is a retrospective single cohort study of all patients with T2D and severe obesity, who underwent DS-type procedures at a single institution from December 2010 to December 2018.